In that respect, replacing domain experts and specialists with learning-based systems and data-driven approaches has had a `democratizing' effect in the broad algorithm design community [1]}, [2]}.
Today, tech-savy generalists and software engineers can deploy state-of-the-art solutions for challenging problems in computer vision [3]}, [4]}, [5]}, natural language [6]}, [7]}, [8]} and speech processing [9]}, [10]}.
We envision the long-term goal of end-to-end learning for combinatorial optimization to be similar:
By making the modelling process intuitive for non-experts, learning-driven OR algorithms will be at the fingertips of more industries and can be personalized to individual users/use cases.
